{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 6 (Main Text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded: jcr_mutiny_conflict_data.dta  |  Rows=1,599,624  Cols=94\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# -----------------------------------------\n",
    "# 0) Lightweight installer if packages are missing\n",
    "# -----------------------------------------\n",
    "\n",
    "\n",
    "try:\n",
    "    import pandas as _pd; import statsmodels as _sm; import matplotlib as _mpl\n",
    "except Exception:\n",
    "    import sys, subprocess\n",
    "    subprocess.check_call(\n",
    "        [sys.executable, \"-m\", \"pip\", \"install\", \"-U\",\n",
    "         \"pandas\", \"statsmodels\", \"matplotlib\"]\n",
    "    )\n",
    "\n",
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import statsmodels.formula.api as smf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ----------------------------\n",
    "# 1) Load data\n",
    "# ----------------------------\n",
    "file_path = Path(r\"C:\\Users\\rmuhi\\OneDrive\\Desktop\\Mutiny_Conflict Paper_All\\Replication File\\jcr_mutiny_conflict_data.dta\")\n",
    "if not file_path.exists():\n",
    "    raise FileNotFoundError(f\"Could not find .dta file at: {file_path}\")\n",
    "\n",
    "data = pd.read_stata(file_path)\n",
    "print(f\"Loaded: {file_path.name}  |  Rows={len(data):,}  Cols={len(data.columns)}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define model formulas with year FE (C(year)) and without time polynomials\n",
    "formula1 = 'dispute2 ~ mutiny_t + lmmilex_persol_t + years_since_coup  + ucdp_civconf_t2 + defense_target + capprop + jdem + majorpower + C(year)'\n",
    "formula2 = 'dispute2 ~ violent_t + lmmilex_persol_t + years_since_coup + ucdp_civconf_t2 + defense_target + capprop + jdem + majorpower + C(year)'\n",
    "formula3 = 'dispute2 ~ size_t_b + lmmilex_persol_t + years_since_coup + ucdp_civconf_t2 + defense_target + capprop + jdem + majorpower + C(year)'\n",
    "formula4 = 'dispute2 ~ C(mduration_t_cat3) + lmmilex_persol_t + years_since_coup + ucdp_civconf_t2 + defense_target + capprop + jdem + majorpower + C(year)'\n",
    "\n",
    "# Drop missing values based on variables used in formula1\n",
    "used_vars = ['mutiny_t', 'lmmilex_persol_t', 'years_since_coup', 'ucdp_civconf_t2', 'defense_target',\n",
    "             'capprop', 'jdem', 'majorpower', 'cold_war', 'year', 'id']\n",
    "data1 = data.dropna(subset=used_vars)\n",
    "\n",
    "model1 = smf.ols(formula1, data=data1).fit(cov_type='cluster', cov_kwds={'groups': data1['id']})\n",
    "\n",
    "# Repeat for other models\n",
    "used_vars2 = ['violent_t'] + used_vars[1:]\n",
    "data2 = data.dropna(subset=used_vars2)\n",
    "model2 = smf.ols(formula2, data=data2).fit(cov_type='cluster', cov_kwds={'groups': data2['id']})\n",
    "\n",
    "used_vars3 = ['size_t_b'] + used_vars[1:]\n",
    "data3 = data.dropna(subset=used_vars3)\n",
    "model3 = smf.ols(formula3, data=data3).fit(cov_type='cluster', cov_kwds={'groups': data3['id']})\n",
    "\n",
    "used_vars4 = ['mduration_t_cat3'] + used_vars[1:]\n",
    "data4 = data.dropna(subset=used_vars4)\n",
    "model4 = smf.ols(formula4, data=data4).fit(cov_type='cluster', cov_kwds={'groups': data4['id']})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# Step 1: Define helper function\n",
    "def extract_effect(result, var_name, label):\n",
    "    return {\n",
    "        'Variable': label,\n",
    "        'Coefficient': result.params.get(var_name),\n",
    "        'SE': result.bse.get(var_name)\n",
    "    }\n",
    "\n",
    "# Step 2: Extract coefficients and SEs\n",
    "plot_data = [\n",
    "    extract_effect(model1, 'mutiny_t', 'Military Mutiny'),\n",
    "    extract_effect(model2, 'violent_t', 'Violent Mutiny'),\n",
    "    extract_effect(model3, 'size_t_b', 'Large Mutiny'),\n",
    "    extract_effect(model4, 'C(mduration_t_cat3)[T.1.0]', 'Short Mutiny'),\n",
    "    extract_effect(model4, 'C(mduration_t_cat3)[T.2.0]', 'Long Mutiny')\n",
    "]\n",
    "\n",
    "df_plot = pd.DataFrame(plot_data)\n",
    "x_positions = range(len(df_plot))\n",
    "\n",
    "# Step 3: Plot\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "ax.set_facecolor('#f5f5f5')\n",
    "\n",
    "ax.errorbar(\n",
    "    x=x_positions,\n",
    "    y=df_plot['Coefficient'],\n",
    "    yerr=1.96 * df_plot['SE'],\n",
    "    fmt='o',\n",
    "    color='black',\n",
    "    ecolor='dimgray',\n",
    "    elinewidth=2,\n",
    "    capsize=4,\n",
    "    capthick=1.4,\n",
    "    markersize=6\n",
    ")\n",
    "\n",
    "ax.axhline(0, color='gray', linestyle='--', linewidth=1)\n",
    "ax.set_xticks(x_positions)\n",
    "ax.set_xticklabels(df_plot['Variable'], rotation=0, fontsize=11)\n",
    "\n",
    "ax.set_ylabel(\"Coefficient Estimate\", fontsize=12)\n",
    "y_min = df_plot['Coefficient'].min() - 0.0019\n",
    "y_max = df_plot['Coefficient'].max() + 0.0022\n",
    "ax.set_ylim(y_min, y_max)\n",
    "ax.set_xlim(-0.7, len(df_plot) - 0.3)\n",
    "\n",
    "ax.grid(True, linestyle=':', color='gray', alpha=0.4)\n",
    "\n",
    "for spine in ['top', 'right', 'left', 'bottom']:\n",
    "    ax.spines[spine].set_visible(False)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
